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The all-fluidic low-pass filtering feature of dilatometric strain sensors has the potential to suppress physiological noise.

BackgroundSignificant national investments have aided the development of practice-based research networks (PBRNs) in both medicine and dentistry. Little evidence has examined the translational impact of these efforts and whether PBRN involvement corresponds to better adoption of best available evidence. This study addresses that gap in knowledge and examines changes in early dental decay among PBRN participants and non-participants with access to the same evidence-based guideline. This study examines the following questions regarding PBRN participation: are practice patterns of providers with PBRN engagement in greater concordance with current evidence? Does provider participation in a PBRNs increase concordance with current evidence? Do providers who participate in PBRN activities disseminate knowledge to their colleagues?MethodsLogistic regression models adjusting for clustering at the clinic and provider levels compared restoration (dental fillings) rates from 2005–2011 among 35 providers in a large staff model practice. All new codes for early-stage caries (dental decay) and co-occurring caries were identified. Treatment was determined by codes occurring up to 6 months following the date of diagnosis. Provider PBRN engagement was determined by study involvement and meeting attendance.ResultsIn 2005, restoration rates were high (79.5%), decreased to 47.6% by 2011 (p < .01), and differed by level of PBRN engagement. In 2005, engaged providers were less likely to use restorations compared to the unengaged (73.1% versus 88.2%; p < .01). Providers with high PBRN involvement decreased use of restorations by 15.4% from 2005 to 2008 (2005: 73%, 2008: 63%; p < .01). Providers with no PBRN involvement decreased use by only 7.5% (2005: 88%, 2008: 82%; p = .041). During the latter half of 2008 following the May PBRN meeting, attendees reduced restorations by 7.5%, compared to a 2.4% among non-attendees (OR = .64, p < .01).ConclusionsBased on actual clinical data, PBRN engagement was associated with practice change consistent with current evidence on treatment of early dental decay. The impact of PBRN engagement was most significant for the most-engaged providers and consistent with a spillover effect onto same-clinic providers who were not PBRN-engaged. PBRNs can generate relevant evidence and expedite translation into practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-014-0177-x) contains supplementary material, which is available to authorized users.

Background: Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. Purpose: Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. Study Type: Retrospective based on prospectively acquired data. Population: Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). Field Strength/Sequence: A 3-T, quantitative double-echo steady state (qDESS). Assessment: Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. Statistical Tests: Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. Results: DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, AE2.4 msec and AE4.0 msec, than the OAI-DESS-trained model, AE4.4 msec and AE5.2 msec. Data Conclusion:The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.